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. 2022 Oct 7;11(19):3122.
doi: 10.3390/foods11193122.

Assessing the Levels of Robusta and Arabica in Roasted Ground Coffee Using NIR Hyperspectral Imaging and FTIR Spectroscopy

Woranitta Sahachairungrueng  1 , Chanyanuch Meechan  2 , Nutchaya Veerachat  2 , Anthony Keith Thompson  3 , Sontisuk Teerachaichayut  2
Affiliations

Assessing the Levels of Robusta and Arabica in Roasted Ground Coffee Using NIR Hyperspectral Imaging and FTIR Spectroscopy

Woranitta Sahachairungrueng et al. Foods. .

Abstract

It has been reported that some brands of roasted ground coffee, whose ingredients are labeled as 100% Arabica coffee, may also contain the cheaper Robusta coffee. Thus, the objective of this research was to test whether near-infrared spectroscopy hyperspectral imaging (NIR-HSI) or Fourier transform infrared spectroscopy (FTIRs) could be used to test whether samples of coffee were pure Arabica or whether they contained Robusta, and if so, what were the levels of Robusta they contained. Qualitative models of both the NIR-HSI and FTIRs techniques were established with support vector machine classification (SVMC). Results showed that the highest levels of accuracy in the prediction set were 98.04 and 97.06%, respectively. Quantitative models of both techniques for predicting the concentration of Robusta in the samples of Arabica with Robusta were established using support vector machine regression (SVMR), which gave the highest levels of accuracy in the prediction set with a coefficient of determination for prediction (Rp2) of 0.964 and 0.956 and root mean square error of prediction (RMSEP) of 5.47 and 6.07%, respectively. It was therefore concluded that the results showed that both techniques (NIR-HSI and FTIRs) have the potential for use in the inspection of roasted ground coffee to classify and determine the respective levels of Arabica and Robusta within the mixture.

Keywords: classification; detection; qualitative; quantitative; spectra.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Schematic view of: (A) NIR-HSI; and (B) FTIRs system.
Figure 2
Figure 2
The average absorbance spectra of pure Arabica and pure Robusta using NIR-HSI: (A) Original spectra; (B) 2nd derivative spectra.
Figure 3
Figure 3
The average original spectra of pure Arabica and pure Robusta using FTIRs.
Figure 4
Figure 4
Score plots of PCA for: (A) pure Arabica and pure Robusta; (B) pure Arabica and Arabica with Robusta using spectral data of NIR-HSI.
Figure 5
Figure 5
Score plots of PCA for: (A) pure Arabica and pure Robusta; (B) pure Arabica and Arabica with Robusta using spectral data of FTIRs.
Figure 6
Figure 6
The confusion matrices of classification in the calibration set by NIR-HSI (A), the prediction set by NIR-HSI (B), the calibration set by FTIRs (C), and the prediction set by FTIRs (D): Class 0 = pure Arabica, Class 1 = Arabica with Robusta.
Figure 7
Figure 7
The scatter plot of actual and predicted adulterant concentration in: (A) the calibration set by NIR-HSI; (B) the prediction set by NIR-HSI; (C) the calibration set by FTIRs; (D) the prediction set by FTIRs.
See this image and copyright information in PMC

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